Resource Allocation Influence on Application Performance in Sliced Testbeds

TL;DR

This work investigates how CPU and RAM resource allocations influence network-slice performance, focusing on observability across two nationwide testbeds. It applies a partial factorial design to quantify latency for write ($W$) and read ($R$) operations on a Cassandra-based network slice deployed on FIBRE-NG and Fabric. The results reveal testbed-dependent resource influences: CPU dominates latency on FIBRE-NG, while RAM dominates latency on Fabric, demonstrating non-uniform behavior across heterogeneous environments and highlighting implications for slice orchestration and auto-scaling strategies. The study underscores the need for cross-testbed observability in resource allocation to ensure predictable network-slice performance in diverse infrastructures.

Abstract

Modern network architectures have shaped market segments, governments, and communities with intelligent and pervasive applications. Ongoing digital transformation through technologies such as softwarization, network slicing, and AI drives this evolution, along with research into Beyond 5G (B5G) and 6G architectures. Network slices require seamless management, observability, and intelligent-native resource allocation, considering user satisfaction, cost efficiency, security, and energy. Slicing orchestration architectures have been extensively studied to accommodate these requirements, particularly in resource allocation for network slices. This study explored the observability of resource allocation regarding network slice performance in two nationwide testbeds. We examined their allocation effects on slicing connectivity latency using a partial factorial experimental method with Central Processing Unit (CPU) and memory combinations. The results reveal different resource impacts across the testbeds, indicating a non-uniform influence on the CPU and memory within the same network slice.

Figures (2)

• Figure 1: Evaluation Method.
• Figure 2: Deployment Time Comparison: FIBRE-NG and Fabric testbeds.